Development of a prototype land use model for statewide transportation planning activities.

Details

• Creators:
  Peng, Zhong-Ren ; Zhao, Li-Yuan ; Yang, Fei
• Corporate Creators:
  University of Florida. Dept. of Urban and Regional Planning
• Corporate Contributors:
  Florida. Department of Transportation
• Subject/TRT Terms:
  • Forecasting
  • Land use models
  • Tables (Data)
  • Manuals
  • Land use modeling
  • Transportation demand modeling
  • Cellular automata
  • Agent-based modeling
  • Land use policy analysis
• Resource Type:
  Tech Report
• Geographical Coverage:
  Florida
• Edition:
  Final; 12/19/08-11/30/11.
• Corporate Publisher:
  University of Florida. Department of Urban and Regional Planning
• Abstract:
  Future land use forecasting is an important input to transportation planning modeling. Traditionally, land use is allocated to individual ; traffic analysis zones (TAZ) based on variables such as the amount of vacant land, zoning restriction, land use planning and policy ; limitations, and accessibility, under an externally estimated control number in population and employment growth at the county level. This ; land use allocation approach does not consider agglomeration factors, the market equilibrium of supply and demand, and is not sensitive to ; different land use and transportation policy changes. To overcome the limitations of this conventional approach, this research project uses ; a new analytical approach, i.e., a combination of cellular automata (CA) and agent-based modeling methods to estimate future land use ; allocation. ; CA models have been used extensively in modeling and simulating complicated spatio-temporal processes like land use change. It ; can model the changes of land use patterns over time and can simulate a variety of spatial processes and influences relevant for land use ; changes. Agent-based models represent the interactions of different decision-making entities. The agent-based model provides a flexible ; representation of heterogeneous decision makers or agents, whose behaviors are potentially influenced by interactions with other agents ; and with their natural and built environment. ; This study uses CA to capture the spatial relationships (e.g., clustering) of land development, as well as agglomeration factors. CA ; represents complicated systems well and is thus a good method to show changes of land use patterns. However, the CA model alone ; cannot sufficiently explain the changes, because CA model is not sensitive to policy variables. Thus, the study also uses agent-based ; models to capture the behavior of each agent, which makes it sensitive to policy changes. Agent-based models, unlike CA, can model ; individual decision-making entities’ behavior as well as their interactions. In addition, this study applies multinomial logit (MNL) model ; to formulate the CA transition rule for different land types, which estimates the probability of future land use for each cell. Therefore, the ; model this study developed is an MNL-CA-Agent land use model, which is called LandSys. ; In the developed LandSys model, land use changes are performed by the CA model, with external drivers as agents. These agents ; include: employer, household, developer and government. The model is estimated and validated using a cell-based representation of land ; (50m x 50m). Then the estimated land use changes can be plugged into Florida Standard Urban Transportation Modeling Structure ; (FSUTMS) models as updated inputs. In response, FSUTMS models update their demand modeling of the transportation system. The ; updated traffic information (e.g., accessibility) is then fed back to the LandSys to capture the interaction between land use and ; transportation and generate more accurate simulation results. LandSys simulates land use change at multiple spatial and temporal scales, as ; well as representing decision making behaviors of households, employment, developers, and government policies. Future land use patterns ; and socioeconomic data can be produced to update those inputs of the transportation model. Policy scenarios, such as mixed land use ; growth management policies, can be simulated and analyzed for decision makers. ; The major advantage of this modeling approach is its integration with FSUTMS models. The feedback cycle between land use and ; transportation models can simulate the interactions between the two. This study employs three indicators to compare the simulation ; accuracy between the integrated framework and standalone FSUTMS models, including link saturation in the transportation network, ; overall vehicle miles traveled (VMT), and vehicle hours traveled (VHT). The results show that the inclusion of the land use and ; transportation feedback in the integrated modeling framework produces better results than the land use or transportation model alone, can ; help modelers better simulate land use-transportation system, and help decision makers better understand the consequences of different ; scenarios of the land use and transportation planning. ; 17. Key Word: Land use modeling, transportation dem
  More +
• Format:
  PDF
• Funding:
  BDK77 977-03
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/23704/dot_23704_DS1.pdf
• File Type:
  [PDF - 1.33 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:13a1770378c307b876cb3eb93be8f0a7ab5719936770bf8dc318f911f3c6003ef2646cd8c12d090f7c6e886ca9bfed772c1bf766c1fe1e0702d8ad23553a0587